Analysis of the character of the variability of heart beats can provide valuable insights that can be useful for diagnosis and monitoring of pathological conditions such as atrial fibrillation (AF) or the presence of other heartbeat characteristics such as ectopic beats. AF is a cardiac disorder that occurs when the heart's upper chambers (the atria) quiver instead of beating effectively. With AF, blood may not be pumped completely out of the atria, allowing blood to pool in the atria and eventually form a clot. If a clot migrates from the left atria it may travel through the arterial system and lodge in the brain, resulting in stroke. A high proportion of AF is asymptomatic and therefore one cannot rely on the patient to report its presence or absence. Assessing the presence of AF is therefore important to establish a diagnosis, choose appropriate therapy and monitor the patient following administration of a therapeutic regimen to determine its effectiveness.
In a normal heart, electrical activity of the atria is quite regular. When the atria contracts, an ECG recorded from electrodes located on the chest will typically show a small deflection corresponding to electrical activity in the atria (i.e. P-wave). When atrial electrical activity reaches the “atrioventricular” (AV) node, it is delayed to allow the atria to finish contracting and expelling blood into the ventricles. Following the delay in the AV node, the electrical activity travels into the ventricles, causing them to contract and pump blood throughout the body.
When AF is present, the AV node is bombarded with a highly variable level of electrical activity from the atria at a rate much faster than its conduction delay can accommodate. As a result, much of the electrical atrial activity from the atria is blocked. Due to this and the stochastic nature of the atrial electrical activity, the AV node stimulates the ventricles in an irregular pattern. AF can therefore usually be detected by evaluating ventricular activity. When physicians examine ECG recordings for the presence of AF, they often describe what they are looking for in the ventricular rhythm as the degree of irregularity in the irregularity. If the irregularity is regular, such as in bigeminy or trigeminy, AF is not present. Likewise if the patient has a strong sinus arrhythmia, as is often the case when a patient is in good physical condition, the rhythm is irregular but is regularly irregular. The irregularity has a regular pattern that correlates with respiration. When AF occurs, the irregularity is highly stochastic.
AF is often transient and therefore requires that the patient be monitored for days or weeks in order to obtain an accurate assessment of the presence of AF and the degree to which it is present (i.e. AF burden). This long-term monitoring often is performed with a small battery-powered wearable device that continuously evaluates the ECG and detects the occurrence of AF and other cardiac arrhythmias. Since the device is battery powered, computational efficiency of the detection algorithm is important.
While approaches to AF detection have been implemented, their implementation has been challenging. For example, detection of AF based upon the irregularity of ventricular rhythm can result in false positive detections when, for example, the patient is experiencing bigeminy, trigeminy or strong sinus arrhythmia. In addition, many approaches are computationally burdensome, which can require large power consumption (e.g., resulting in the need for a large battery and hence large device size and weight), and suffer from short battery life.